CausalImpact: Inferring Causal Effects using Bayesian Structural Time-Series Models

Implements a Bayesian approach to causal impact estimation in time series, as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>. See the package documentation on GitHub <https://google.github.io/CausalImpact/> to get started.

Version: 1.3.0
Depends: bsts (≥ 0.9.0)
Imports: assertthat (≥ 0.2.0), Boom, ggplot2, zoo
Suggests: covr, knitr, rmarkdown, testthat
Published: 2022-11-09
Author: Kay H. Brodersen, Alain Hauser
Maintainer: Alain Hauser <alhauser at google.com>
License: Apache License 2.0 | file LICENSE
Copyright: Copyright (C) 2014-2022 Google, Inc.
URL: https://google.github.io/CausalImpact/
NeedsCompilation: no
Citation: CausalImpact citation info
Materials: README
In views: Bayesian, CausalInference
CRAN checks: CausalImpact results

Documentation:

Reference manual: CausalImpact.pdf
Vignettes: CausalImpact

Downloads:

Package source: CausalImpact_1.3.0.tar.gz
Windows binaries: r-devel: CausalImpact_1.3.0.zip, r-release: CausalImpact_1.3.0.zip, r-oldrel: CausalImpact_1.3.0.zip
macOS binaries: r-release (arm64): CausalImpact_1.3.0.tgz, r-oldrel (arm64): CausalImpact_1.3.0.tgz, r-release (x86_64): CausalImpact_1.3.0.tgz
Old sources: CausalImpact archive

Reverse dependencies:

Reverse imports: MarketMatching, SPORTSCausal

Linking:

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